A significant challenge to enabling Augmented Reality (AR) on mobile phones or other mobile platforms is the problem of estimating and tracking the camera pose in real-time. Pose tracking for AR applications has very demanding requirements: it must deliver full six degrees of freedom, give absolute measurements with respect to a given coordinate system, be very robust and run in real-time. Of interest are methods to compute pose using computer vision (CV) based approaches, which rely on first detecting and, subsequently, tracking objects within the camera view. In particular, it is essential for the tracking operations to be completed within the camera frame duration, in order for the augmentations rendered by the graphics engine to be tightly aligned with real (world) objects. Furthermore, it is important that object detection runs in parallel with tracking to identify new objects that appear within the camera view.
Recently, mobile phones have become increasingly attractive for AR. With the built-in camera as the primary sensor, phones facilitate intuitive point-and-shoot interaction with the environment. However, the limited computational capabilities of the mobile phone CPU require increased efficiencies. Moreover, the fidelity of the user interface demands that the real-time characteristics of the pose tracking should not be affected by the image content.